<<<<<<< HEAD ||||||| merged common ancestors ======= >>>>>>> 371ff05b569a6264d9798b347807497f5edc6dbd Core microbiome

Core microbiome

See also related functions for the analysis of rare and variable taxa (noncore_members; noncore_abundance; rare_members; rare_abundance; low_abundance).

HITChip Data

Load example data:

Prevalence of taxonomic groups

Relative population frequencies; at 1% compositional abundance threshold:

## Roseburia intestinalis et rel.     Eubacterium hallii et rel. 
##                      1.0000000                      1.0000000 
##     Clostridium nexile et rel.     Ruminococcus obeum et rel. 
##                      1.0000000                      0.9772727 
##   Coprococcus eutactus et rel.  Ruminococcus lactaris et rel. 
##                      0.9772727                      0.9545455

Absolute population frequencies (sample count):

## Roseburia intestinalis et rel.     Eubacterium hallii et rel. 
##                             44                             44 
##     Clostridium nexile et rel.     Ruminococcus obeum et rel. 
##                             44                             43 
##   Coprococcus eutactus et rel.  Ruminococcus lactaris et rel. 
##                             43                             42

Core microbiota analysis

If you only need the names of the core taxa, do as follows. This returns the taxa that exceed the given prevalence and detection thresholds.

A full phyloseq object of the core microbiota is obtained as follows:

Retrieving the associated taxa names from the phyloseq object:

Core abundance and diversity

Total core abundance in each sample (sum of abundances of the core members):

Core visualization

Core heatmaps

This visualization method has been used for instance in Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. Shetty et al. FEMS Microbiology Reviews fuw045, 2017.

Note that you can order the taxa on the heatmap with the taxa.order argument.

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# Core with compositionals:
prevalences <- seq(.05, 1, .05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

# Also define gray color palette
gray <- gray(seq(0,1,length=5))
p <- plot_core(pseq.rel, plot.type = "heatmap", colours = gray,
    prevalences = prevalences, detections = detections) +
    xlab("Detection Threshold (Relative Abundance (%))")
print(p)    


# Core with absolute counts and horizontal view:
# and minimum population prevalence (given as percentage)
detections <- 10^seq(log10(1), log10(max(abundances(pseq))/10), length = 10)

library(RColorBrewer)
p <- plot_core(pseq, plot.type = "heatmap", 
             prevalences = prevalences,
             detections = detections,
         colours = rev(brewer.pal(5, "Spectral")),
         min.prevalence = .2, horizontal = TRUE)
print(p)

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# Core with compositionals:
prevalences <- seq(.05, 1, .05)
detections <- 10^seq(log10(1e-3), log10(.2), length = 10)

# Also define gray color palette
gray <- gray(seq(0,1,length=5))
p <- plot_core(pseq.rel, plot.type = "heatmap", colours = gray,
    prevalences = prevalences, detections = detections) +
    xlab("Detection Threshold (Relative Abundance (%))")
print(p)    


# Core with absolute counts and horizontal view:
# and minimum population prevalence (given as percentage)
detections <- 10^seq(log10(1), log10(max(abundances(pseq))/10), length = 10)

library(RColorBrewer)
p <- plot_core(pseq, plot.type = "heatmap", 
             prevalences = prevalences,
             detections = detections,
         colours = rev(brewer.pal(5, "Spectral")),
         min.prevalence = .2, horizontal = TRUE)
print(p)

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Core Microbiota using Amplicon data

Make phyloseq object

This tutorial is useful for analysis of output files from (Mothur), (QIIME or QIIME2) or any tool that gives a biom file as output. There is also a simple way to read comma seperated (*.csv) files.

Simple comma seperated files:

Biom file:

Mothur shared OTUs and Consensus Taxonomy:

Now, we proceed to core microbiota analysis.

Core microbiota analysis

Here the data from (Halfvarson et al. Nature Microbiology 2, 2017) will be used and only healthy samples will be included.

Prevalence of taxonomic groups

Relative population frequencies; at 1% compositional abundance threshold:

We can see that only OTU ids are listed with no taxonomic information. Absolute population frequencies (sample count):

Core abundance and diversity

Total core abundance in each sample (sum of abundances of the core members):

Core visualization

Core heatmaps

This visualization method has been used for instance in Intestinal microbiome landscaping: Insight in community assemblage and implications for microbial modulation strategies. Shetty et al. FEMS Microbiology Reviews fuw045, 2017.

Note that you can order the taxa on the heatmap with the order.taxa argument.

As it can be seen, we see only OTu IDs and this may not be useful to interpret the data. We need to repreoccess this figure to include taxonomic information. We can do this as follows:

<<<<<<< HEAD

Leo Lahti, Sudarshan Shetty et al. 2018-11-19

||||||| merged common ancestors

Leo Lahti, Sudarshan Shetty et al. 2018-08-13

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Leo Lahti, Sudarshan Shetty et al. 2018-11-15

>>>>>>> 371ff05b569a6264d9798b347807497f5edc6dbd